Learning rates of support vector machine classifier for density level detection

Authors:
Feilong Cao;Xing Xing;Jianwei Zhao
Affiliations:
Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, P.R. China;Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, P.R. China;Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, P.R. China
Venue:
Neurocomputing
Year:
2012

Citing 8
Cited 4

Learning distributions by their density levels: a paradigm for learning without a teacher

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Learning in Neural Networks: Theoretical Foundations

Learning in Neural Networks: Theoretical Foundations
Support Vector Machine Soft Margin Classifiers: Error Analysis

The Journal of Machine Learning Research
A Classification Framework for Anomaly Detection

The Journal of Machine Learning Research
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming

Neural Computation
Learning Rates of Least-Square Regularized Regression

Foundations of Computational Mathematics
Multi-kernel regularized classifiers

Journal of Complexity

On the performance of regularized regression learning in Hilbert space

Neurocomputing
Granular support vector machine based on mixed measure

Neurocomputing
Error analysis of coefficient-based regularized algorithm for density-level detection

Neural Computation
Generalization performance of support vector classifiers for density level detection

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we consider the learning rates of support vector machines (SVMs) classifier for density level detection (DLD) problem. Using an established classification framework, we get error decomposition which consists of regularization error and sample error. Based on the decomposition, we obtain learning rates of SVMs classifier for DLD under some assumptions.